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TurkuNLP/
sbert-uncased-finnish-paraphrase

Name: sbert-uncased-finnish-paraphrase
Availability: InStock

sentence-transformers

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Uncased Finnish Sentence BERT model

Finnish Sentence BERT trained from FinBERT. A demo on retrieving the most similar sentences from a dataset of 400 million sentences using the cased model can be found here.

Training

Library: sentence-transformers
FinBERT model: TurkuNLP/bert-base-finnish-uncased-v1
Data: The data provided here, including the Finnish Paraphrase Corpus and the automatically collected paraphrase candidates (500K positive and 5M negative)
Pooling: mean pooling
Task: Binary prediction, whether two sentences are paraphrases or not. Note: the labels 3 and 4 are considered paraphrases, and labels 1 and 2 non-paraphrases. Details on labels

Usage

The same as in HuggingFace documentation. Either through SentenceTransformer or HuggingFace Transformers

SentenceTransformer

python

from sentence_transformers import SentenceTransformer
sentences = ["Tämä on esimerkkilause.", "Tämä on toinen lause."]

model = SentenceTransformer('TurkuNLP/sbert-uncased-finnish-paraphrase')
embeddings = model.encode(sentences)
print(embeddings)

HuggingFace Transformers

python

from transformers import AutoTokenizer, AutoModel
import torch


# Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


# Sentences we want sentence embeddings for
sentences = ["Tämä on esimerkkilause.", "Tämä on toinen lause."]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('TurkuNLP/sbert-uncased-finnish-paraphrase')
model = AutoModel.from_pretrained('TurkuNLP/sbert-uncased-finnish-paraphrase')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)

Evaluation Results

A publication detailing the evaluation results is currently being drafted.

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': True}) with Transformer model: BertModel
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
    )

Citing & Authors

While the publication is being drafted, please cite this page.

References

J. Kanerva, F. Ginter, LH. Chang, I. Rastas, V. Skantsi, J. Kilpeläinen, HM. Kupari, J. Saarni, M. Sevón, and O. Tarkka. Finnish Paraphrase Corpus. In NoDaLiDa 2021, 2021.
N. Reimers and I. Gurevych. Sentence-BERT: Sentence embeddings using Siamese BERT-networks. In EMNLP-IJCNLP, pages 3982–3992, 2019.
A. Virtanen, J. Kanerva, R. Ilo, J. Luoma, J. Luotolahti, T. Salakoski, F. Ginter, and S. Pyysalo. Multilingual is not enough: BERT for Finnish. arXiv preprint arXiv:1912.07076, 2019.